Geocomputation Apply
R
Geocomputation
Library packages
rm(list = ls())
library(sf)Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUElibrary(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.0 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(rgdal)필요한 패키지를 로딩중입니다: sp
Please note that rgdal will be retired during 2023,
plan transition to sf/stars/terra functions using GDAL and PROJ
at your earliest convenience.
See https://r-spatial.org/r/2022/04/12/evolution.html and https://github.com/r-spatial/evolution
rgdal: version: 1.6-6, (SVN revision 1201)
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 3.5.2, released 2022/09/02
Path to GDAL shared files: C:/Users/sungi/AppData/Local/R/win-library/4.2/rgdal/gdal
GDAL binary built with GEOS: TRUE
Loaded PROJ runtime: Rel. 8.2.1, January 1st, 2022, [PJ_VERSION: 821]
Path to PROJ shared files: C:/Users/sungi/AppData/Local/R/win-library/4.2/rgdal/proj
PROJ CDN enabled: FALSE
Linking to sp version:1.6-0
To mute warnings of possible GDAL/OSR exportToProj4() degradation,
use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.library(plotly)
다음의 패키지를 부착합니다: 'plotly'
The following object is masked from 'package:ggplot2':
last_plot
The following object is masked from 'package:stats':
filter
The following object is masked from 'package:graphics':
layoutlibrary(ggtext)
library(tmap)Load data
Following data is geographic data for Chungcheongnam-do.
http://data.nsdi.go.kr/dataset/15145
| EMD_CD | EMD_NM | SGG_OID | COL_ADM_SE | GID | geometry |
|---|---|---|---|---|---|
| 읍면동 코드 | 읍면동 이름 | 시군구 코드 | 시 코드 | GID | 지리정보 |
Convert data type (sp -> sf)
data_sp <- readOGR("C:/trainsets_2/LSMD_ADM_SECT_UMD_충남/LSMD_ADM_SECT_UMD_44.shp",encoding = "euc-kr")Warning: OGR support is provided by the sf and terra packages among othersWarning: OGR support is provided by the sf and terra packages among othersWarning: OGR support is provided by the sf and terra packages among othersWarning: GDAL support is provided by the sf and terra packages among othersWarning: GDAL support is provided by the sf and terra packages among others
Warning: GDAL support is provided by the sf and terra packages among othersWarning: OGR support is provided by the sf and terra packages among othersWarning: OGR support is provided by the sf and terra packages among othersWarning: OGR support is provided by the sf and terra packages among othersWarning: OGR support is provided by the sf and terra packages among othersOGR data source with driver: ESRI Shapefile
Source: "C:\trainsets_2\LSMD_ADM_SECT_UMD_충남\LSMD_ADM_SECT_UMD_44.shp", layer: "LSMD_ADM_SECT_UMD_44"
with 316 features
It has 5 fieldsWarning: GDAL support is provided by the sf and terra packages among othersWarning: GDAL support is provided by the sf and terra packages among others
Warning: GDAL support is provided by the sf and terra packages among othersdata_sf = st_as_sf(data_sp)
data_sf %>% head()Simple feature collection with 6 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 911558.7 ymin: 1829456 xmax: 934941.7 ymax: 1853055
Projected CRS: Korea 2000 / Unified CS
EMD_CD EMD_NM SGG_OID COL_ADM_SE GID geometry
0 44800360 결성면 3817 44800 979 MULTIPOLYGON (((911891.8 18...
1 44800350 은하면 3816 44800 980 MULTIPOLYGON (((920373.2 18...
2 44800340 장곡면 3815 44800 981 MULTIPOLYGON (((931934 1838...
3 44800330 홍동면 3814 44800 982 MULTIPOLYGON (((931821 1843...
4 44800320 금마면 3813 44800 983 MULTIPOLYGON (((931724.3 18...
5 44800256 홍북읍 3812 44800 984 MULTIPOLYGON (((931724.3 18...data_sf %>% ggplot(aes(fill = COL_ADM_SE))+
geom_sf()+
theme_minimal()+
labs(title = "충청남도")
Filter Place of Interest
cheonan <- data_sf %>%
filter(COL_ADM_SE=="44130")
cheonan_seobuk <- cheonan %>%
filter(substr(EMD_CD,1,5)=="44133")
cheonan_dongnam <- cheonan %>%
filter(substr(EMD_CD,1,5)=="44131")cheonan %>% ggplot(aes(fill=substr(EMD_CD,1,5)))+
geom_sf()+
theme_minimal()+
labs(title = "천안시 (구별)")+
scale_fill_discrete(name = "구",
labels = c("동남구","서북구"))
chsb <- cheonan_seobuk %>% ggplot(aes(fill=EMD_NM))+
geom_sf()+
geom_sf_text(mapping = aes(label = EMD_NM))+
labs(title = "천안시 서북구 (읍면동)")+
theme_minimal()+
scale_fill_discrete(name = "읍면동")
chsb %>% ggplotly()chdn <- cheonan_dongnam %>% ggplot(aes(fill=EMD_NM))+
geom_sf()+
geom_sf_text(mapping = aes(label = EMD_NM))+
labs(title = "천안시 동남구 (읍면동)")+
theme_minimal()+
scale_fill_discrete(name = "읍면동")
chdn %>% ggplotly()건축년도
data <- read.csv("C:/trainsets_2/GEOCOMPS.csv")
data_sf <- data %>% st_as_sf(coords = c("Longitude","Latitude"))
st_crs(data_sf) <- 4737
st_crs(data_sf)Coordinate Reference System:
User input: EPSG:4737
wkt:
GEOGCRS["Korea 2000",
DATUM["Geocentric datum of Korea",
ELLIPSOID["GRS 1980",6378137,298.257222101,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["Republic of Korea (South Korea) - onshore and offshore."],
BBOX[28.6,122.71,40.27,134.28]],
ID["EPSG",4737]]cheonan <- cheonan %>%
filter(EMD_NM!="광덕면")
gn <- ggplot()+
geom_sf(data = cheonan,fill=NA)+
geom_sf(data = data_sf,mapping = aes(color = 건축년도))+
scale_color_gradient(low = "blue", high = "red")+
theme_minimal()
gn %>% ggplotly()아파트별 평단가
ggplotly
data_nm_raw <- read.csv("C:/Users/sungi/Documents/linear_model_dataset_R.csv")
data_nm_raw <- data_nm_raw %>% mutate(평단가 = 거래금액/전용면적)
data_nm <- data_nm_raw %>%
group_by(아파트) %>%
summarize(평균거래액 = mean(평단가) %>% as.integer(),
Latitude = mean(Latitude),
Longitude = mean(Longitude)) %>%
mutate(정보 = paste(아파트,평균거래액,sep = ", 평단가 :"))
data_nm_sf <- data_nm %>% st_as_sf(coords = c("Longitude","Latitude"))
st_crs(data_nm_sf) <- 4737
cheonan <- cheonan %>%
filter(EMD_NM!="광덕면")
gn <- ggplot()+
geom_sf(data = cheonan,color = "black")+
geom_sf(data = data_nm_sf,aes(fill = 정보))+
theme_minimal()+
theme(legend.position = "none")
gn %>% ggplotly()Tmap library
tmap_mode("view")tmap mode set to interactive viewingtmap_options(check.and.fix = TRUE)
tm_shape(cheonan["EMD_NM"])+
tm_polygons(col="white",alpha = 0.3)+
tm_shape(data_nm_sf)+
tm_symbols(shape = 2, col = "red", size = 0.05)+
tm_layout(main.title = "High points in New Zealand", main.title.size = 1,
bg.color = "lightblue")Warning: The shape cheonan["EMD_NM"] is invalid (after reprojection). See
sf::st_is_validSymbol shapes other than circles or icons are not supported in view mode.